



from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


a      :  List[str]			     =			logging.get_logger(__name__)  # pylint: disable=invalid-name

a      :  str			     =			'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'







def  lowerCAmelCase_	(lowerCAmelCase__:		Dict		,						lowerCAmelCase__:		Optional[int]		,						lowerCAmelCase__:		Optional[Any]=8  ):





			"""simple docstring"""


			UpperCAmelCase_:		Union[str, Any]							      =     height // scale_factor**2
			if height % scale_factor**2 != 0:
						new_height += 1
			UpperCAmelCase_:		Union[str, Any]							      =     width // scale_factor**2
			if width % scale_factor**2 != 0:
						new_width += 1
			return new_height * scale_factor, new_width * scale_factor







def  lowerCAmelCase_	(lowerCAmelCase__:		int		,						lowerCAmelCase__:		Optional[int]=5_1_2		,						lowerCAmelCase__:		Union[str, Any]=5_1_2  ):





			"""simple docstring"""


			UpperCAmelCase_:		List[str]							      =     pil_image.resize((w, h)		,						resample=Image.BICUBIC		,						reducing_gap=1  )
			UpperCAmelCase_:		Any							      =     np.array(pil_image.convert("""RGB"""  )  )
			UpperCAmelCase_:		Optional[Any]							      =     arr.astype(np.floataa  ) / 127.5 - 1
			UpperCAmelCase_:		Optional[Any]							      =     np.transpose(lowerCAmelCase__		,						[2, 0, 1]  )
			UpperCAmelCase_:		Dict							      =     torch.from_numpy(lowerCAmelCase__  ).unsqueeze(0  )
			return image







class 				_a				(   _lowerCAmelCase					):

			def __init__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   )       ->   int:
						super().__init__()

						self.register_modules(
						    unet=SCREAMING_SNAKE_CASE_,   scheduler=SCREAMING_SNAKE_CASE_,   movq=SCREAMING_SNAKE_CASE_,   )
						UpperCAmelCase_:		List[str]							      =     2 ** (len(self.movq.config.block_out_channels					) - 1)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   List[str]:
						# get the original timestep using init_timestep
						UpperCAmelCase_:		Dict							      =     min(int(num_inference_steps * strength					),   SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		str							      =     max(num_inference_steps - init_timestep,   0					)
						UpperCAmelCase_:		Union[str, Any]							      =     self.scheduler.timesteps[t_start:]

						return timesteps, num_inference_steps - t_start

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_=None					)       ->   Optional[Any]:
						if not isinstance(SCREAMING_SNAKE_CASE_,   (torch.Tensor, PIL.Image.Image, list)					):
									raise ValueError(
									    f'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(SCREAMING_SNAKE_CASE_					)}'					)

						UpperCAmelCase_:		Optional[Any]							      =     image.to(device=SCREAMING_SNAKE_CASE_,   dtype=SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Union[str, Any]							      =     batch_size * num_images_per_prompt

						if image.shape[1] == 4:
									UpperCAmelCase_:		Union[str, Any]							      =     image

						else:
									if isinstance(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					) and len(SCREAMING_SNAKE_CASE_					) != batch_size:
												raise ValueError(
												    f'You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE_					)}, but requested an effective batch'
												    f' size of {batch_size}. Make sure the batch size matches the length of the generators.'					)

									elif isinstance(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					):
												UpperCAmelCase_:		List[str]							      =     [
												    self.movq.encode(image[i : i + 1]					).latent_dist.sample(generator[i]					) for i in range(SCREAMING_SNAKE_CASE_					)
												]
												UpperCAmelCase_:		Any							      =     torch.cat(SCREAMING_SNAKE_CASE_,   dim=0					)
									else:
												UpperCAmelCase_:		Dict							      =     self.movq.encode(SCREAMING_SNAKE_CASE_					).latent_dist.sample(SCREAMING_SNAKE_CASE_					)

									UpperCAmelCase_:		int							      =     self.movq.config.scaling_factor * init_latents

						UpperCAmelCase_:		List[str]							      =     torch.cat([init_latents],   dim=0					)

						UpperCAmelCase_:		str							      =     init_latents.shape
						UpperCAmelCase_:		List[str]							      =     randn_tensor(SCREAMING_SNAKE_CASE_,   generator=SCREAMING_SNAKE_CASE_,   device=SCREAMING_SNAKE_CASE_,   dtype=SCREAMING_SNAKE_CASE_					)

						# get latents
						UpperCAmelCase_:		List[str]							      =     self.scheduler.add_noise(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Optional[Any]							      =     init_latents

						return latents

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_=0					)       ->   Optional[int]:
						if is_accelerate_available():
									from accelerate import cpu_offload
						else:
									raise ImportError("""Please install accelerate via `pip install accelerate`"""					)

						UpperCAmelCase_:		Union[str, Any]							      =     torch.device(f'cuda:{gpu_id}'					)

						UpperCAmelCase_:		List[str]							      =     [
						    self.unet,
						    self.movq,
						]
						for cpu_offloaded_model in models:
									if cpu_offloaded_model is not None:
												cpu_offload(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_=0					)       ->   Tuple:
						if is_accelerate_available() and is_accelerate_version(""">=""",   """0.17.0.dev0"""					):
									from accelerate import cpu_offload_with_hook
						else:
									raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""					)

						UpperCAmelCase_:		Union[str, Any]							      =     torch.device(f'cuda:{gpu_id}'					)

						if self.device.type != "cpu":
									self.to("""cpu""",   silence_dtype_warnings=SCREAMING_SNAKE_CASE_					)
									torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

						UpperCAmelCase_:		Optional[Any]							      =     None
						for cpu_offloaded_model in [self.unet, self.movq]:
									UpperCAmelCase_      ,							UpperCAmelCase_:		Union[str, Any]							      =     cpu_offload_with_hook(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   prev_module_hook=SCREAMING_SNAKE_CASE_					)

						# We'll offload the last model manually.
						UpperCAmelCase_:		List[str]							      =     hook

			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def      __snake_case       (self					)       ->   Any:
						if not hasattr(self.unet,   """_hf_hook"""					):
									return self.device
						for module in self.unet.modules():
									if (
									    hasattr(SCREAMING_SNAKE_CASE_,   """_hf_hook"""					)
									    and hasattr(module._hf_hook,   """execution_device"""					)
									    and module._hf_hook.execution_device is not None
									):
												return torch.device(module._hf_hook.execution_device					)
						return self.device

			@torch.no_grad()
			@replace_example_docstring(SCREAMING_SNAKE_CASE_					)
			def __call__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = 512,   SCREAMING_SNAKE_CASE_ = 512,   SCREAMING_SNAKE_CASE_ = 100,   SCREAMING_SNAKE_CASE_ = 4.0,   SCREAMING_SNAKE_CASE_ = 0.3,   SCREAMING_SNAKE_CASE_ = 1,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = "pil",   SCREAMING_SNAKE_CASE_ = True,   )       ->   Tuple:
						UpperCAmelCase_:		Dict							      =     self._execution_device

						UpperCAmelCase_:		Optional[int]							      =     guidance_scale > 1.0

						if isinstance(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					):
									UpperCAmelCase_:		int							      =     torch.cat(SCREAMING_SNAKE_CASE_,   dim=0					)
						UpperCAmelCase_:		int							      =     image_embeds.shape[0]
						if isinstance(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					):
									UpperCAmelCase_:		Union[str, Any]							      =     torch.cat(SCREAMING_SNAKE_CASE_,   dim=0					)

						if do_classifier_free_guidance:
									UpperCAmelCase_:		str							      =     image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_,   dim=0					)
									UpperCAmelCase_:		Any							      =     negative_image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_,   dim=0					)

						UpperCAmelCase_:		Optional[Any]							      =     torch.cat([negative_image_embeds, image_embeds],   dim=0					).to(dtype=self.unet.dtype,   device=SCREAMING_SNAKE_CASE_					)

						if not isinstance(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					):
									UpperCAmelCase_:		Optional[int]							      =     [image]
						if not all(isinstance(SCREAMING_SNAKE_CASE_,   (PIL.Image.Image, torch.Tensor)					) for i in image					):
									raise ValueError(
									    f'Input is in incorrect format: {[type(SCREAMING_SNAKE_CASE_					) for i in image]}. Currently, we only support  PIL image and pytorch tensor'					)

						UpperCAmelCase_:		str							      =     torch.cat([prepare_image(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					) for i in image],   dim=0					)
						UpperCAmelCase_:		Optional[Any]							      =     image.to(dtype=image_embeds.dtype,   device=SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Tuple							      =     self.movq.encode(SCREAMING_SNAKE_CASE_					)["""latents"""]
						UpperCAmelCase_:		Optional[int]							      =     latents.repeat_interleave(SCREAMING_SNAKE_CASE_,   dim=0					)
						self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_,   device=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_      ,							UpperCAmelCase_:		int							      =     self.get_timesteps(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		int							      =     timesteps[:1].repeat(batch_size * num_images_per_prompt					)
						UpperCAmelCase_      ,							UpperCAmelCase_:		int							      =     downscale_height_and_width(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   self.movq_scale_factor					)
						UpperCAmelCase_:		str							      =     self.prepare_latents(
						    SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   image_embeds.dtype,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
						for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_					)					):
									# expand the latents if we are doing classifier free guidance
									UpperCAmelCase_:		int							      =     torch.cat([latents] * 2					) if do_classifier_free_guidance else latents

									UpperCAmelCase_:		Tuple							      =     {"""image_embeds""": image_embeds}
									UpperCAmelCase_:		Tuple							      =     self.unet(
									    sample=SCREAMING_SNAKE_CASE_,   timestep=SCREAMING_SNAKE_CASE_,   encoder_hidden_states=SCREAMING_SNAKE_CASE_,   added_cond_kwargs=SCREAMING_SNAKE_CASE_,   return_dict=SCREAMING_SNAKE_CASE_,   )[0]

									if do_classifier_free_guidance:
												UpperCAmelCase_      ,							UpperCAmelCase_:		List[str]							      =     noise_pred.split(latents.shape[1],   dim=1					)
												UpperCAmelCase_      ,							UpperCAmelCase_:		Optional[Any]							      =     noise_pred.chunk(2					)
												UpperCAmelCase_      ,							UpperCAmelCase_:		Any							      =     variance_pred.chunk(2					)
												UpperCAmelCase_:		str							      =     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
												UpperCAmelCase_:		str							      =     torch.cat([noise_pred, variance_pred_text],   dim=1					)

									if not (
									    hasattr(self.scheduler.config,   """variance_type"""					)
									    and self.scheduler.config.variance_type in ["learned", "learned_range"]
									):
												UpperCAmelCase_      ,							UpperCAmelCase_:		Union[str, Any]							      =     noise_pred.split(latents.shape[1],   dim=1					)

									# compute the previous noisy sample x_t -> x_t-1
									UpperCAmelCase_:		Dict							      =     self.scheduler.step(
									    SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   generator=SCREAMING_SNAKE_CASE_,   )[0]

						# post-processing
						UpperCAmelCase_:		Tuple							      =     self.movq.decode(SCREAMING_SNAKE_CASE_,   force_not_quantize=SCREAMING_SNAKE_CASE_					)["""sample"""]

						if output_type not in ["pt", "np", "pil"]:
									raise ValueError(f'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'					)

						if output_type in ["np", "pil"]:
									UpperCAmelCase_:		List[str]							      =     image * 0.5 + 0.5
									UpperCAmelCase_:		Tuple							      =     image.clamp(0,   1					)
									UpperCAmelCase_:		List[Any]							      =     image.cpu().permute(0,   2,   3,   1					).float().numpy()

						if output_type == "pil":
									UpperCAmelCase_:		Tuple							      =     self.numpy_to_pil(SCREAMING_SNAKE_CASE_					)

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_					)











from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


a      :  Dict			     =			logging.get_logger(__name__)







class 				_a				(   _lowerCAmelCase					):
			A      		=	['''pixel_values''']

			def __init__(self,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = PIL.Image.BICUBIC,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = 1 / 255,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   None:
						super().__init__(**SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[Any]							      =     size if size is not None else {"""height""": 256, """width""": 256}
						UpperCAmelCase_:		Optional[Any]							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		int							      =     crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
						UpperCAmelCase_:		Dict							      =     get_size_dict(SCREAMING_SNAKE_CASE_,   param_name="""crop_size"""					)

						UpperCAmelCase_:		int							      =     do_resize
						UpperCAmelCase_:		Any							      =     size
						UpperCAmelCase_:		List[Any]							      =     resample
						UpperCAmelCase_:		int							      =     do_center_crop
						UpperCAmelCase_:		Dict							      =     crop_size
						UpperCAmelCase_:		Tuple							      =     do_rescale
						UpperCAmelCase_:		Tuple							      =     rescale_factor
						UpperCAmelCase_:		Dict							      =     do_normalize
						UpperCAmelCase_:		Dict							      =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						UpperCAmelCase_:		int							      =     image_std if image_std is not None else IMAGENET_STANDARD_STD

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = PIL.Image.BICUBIC,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   np.ndarray:
						UpperCAmelCase_:		Tuple							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						if "height" not in size or "width" not in size:
									raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'					)
						return resize(
						    SCREAMING_SNAKE_CASE_,   size=(size["""height"""], size["""width"""]),   resample=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   np.ndarray:
						UpperCAmelCase_:		Optional[int]							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						if "height" not in size or "width" not in size:
									raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'					)
						return center_crop(SCREAMING_SNAKE_CASE_,   size=(size["""height"""], size["""width"""]),   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   int:
						return rescale(SCREAMING_SNAKE_CASE_,   scale=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   np.ndarray:
						return normalize(SCREAMING_SNAKE_CASE_,   mean=SCREAMING_SNAKE_CASE_,   std=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_=None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST,   **SCREAMING_SNAKE_CASE_,   )       ->   PIL.Image.Image:
						UpperCAmelCase_:		Any							      =     do_resize if do_resize is not None else self.do_resize
						UpperCAmelCase_:		Dict							      =     resample if resample is not None else self.resample
						UpperCAmelCase_:		Tuple							      =     do_center_crop if do_center_crop is not None else self.do_center_crop
						UpperCAmelCase_:		str							      =     do_rescale if do_rescale is not None else self.do_rescale
						UpperCAmelCase_:		List[Any]							      =     rescale_factor if rescale_factor is not None else self.rescale_factor
						UpperCAmelCase_:		List[Any]							      =     do_normalize if do_normalize is not None else self.do_normalize
						UpperCAmelCase_:		Optional[Any]							      =     image_mean if image_mean is not None else self.image_mean
						UpperCAmelCase_:		Dict							      =     image_std if image_std is not None else self.image_std

						UpperCAmelCase_:		Any							      =     size if size is not None else self.size
						UpperCAmelCase_:		Optional[Any]							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Tuple							      =     crop_size if crop_size is not None else self.crop_size
						UpperCAmelCase_:		Optional[int]							      =     get_size_dict(SCREAMING_SNAKE_CASE_,   param_name="""crop_size"""					)

						UpperCAmelCase_:		str							      =     make_list_of_images(SCREAMING_SNAKE_CASE_					)

						if not valid_images(SCREAMING_SNAKE_CASE_					):
									raise ValueError(
									    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
									    """torch.Tensor, tf.Tensor or jax.ndarray."""					)

						if do_resize and size is None or resample is None:
									raise ValueError("""Size and resample must be specified if do_resize is True."""					)

						if do_center_crop and crop_size is None:
									raise ValueError("""Crop size must be specified if do_center_crop is True."""					)

						if do_rescale and rescale_factor is None:
									raise ValueError("""Rescale factor must be specified if do_rescale is True."""					)

						if do_normalize and (image_mean is None or image_std is None):
									raise ValueError("""Image mean and std must be specified if do_normalize is True."""					)

						# All transformations expect numpy arrays.
						UpperCAmelCase_:		Tuple							      =     [to_numpy_array(SCREAMING_SNAKE_CASE_					) for image in images]

						if do_resize:
									UpperCAmelCase_:		int							      =     [self.resize(image=SCREAMING_SNAKE_CASE_,   size=SCREAMING_SNAKE_CASE_,   resample=SCREAMING_SNAKE_CASE_					) for image in images]

						if do_center_crop:
									UpperCAmelCase_:		Dict							      =     [self.center_crop(image=SCREAMING_SNAKE_CASE_,   size=SCREAMING_SNAKE_CASE_					) for image in images]

						if do_rescale:
									UpperCAmelCase_:		Optional[int]							      =     [self.rescale(image=SCREAMING_SNAKE_CASE_,   scale=SCREAMING_SNAKE_CASE_					) for image in images]

						if do_normalize:
									UpperCAmelCase_:		int							      =     [self.normalize(image=SCREAMING_SNAKE_CASE_,   mean=SCREAMING_SNAKE_CASE_,   std=SCREAMING_SNAKE_CASE_					) for image in images]

						UpperCAmelCase_:		Optional[Any]							      =     [to_channel_dimension_format(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					) for image in images]

						UpperCAmelCase_:		Optional[Any]							      =     {"""pixel_values""": images}
						return BatchFeature(data=SCREAMING_SNAKE_CASE_,   tensor_type=SCREAMING_SNAKE_CASE_					)






